Bioinformatics Research Analyst - Department of Oncology

Wayne State University is searching for an experienced
Bioinformatics Research Analyst
- Department of Oncology atits Detroit campus location.

Wayne State is a premier, public, urban research university locatedin the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Ourdeep-rooted commitment to excellence, collaboration, integrity,diversity and inclusion creates exceptional educational opportunities which prepare students for success in a global society.

Provide computational and analytic support for a cancer geneticepidemiology research program focused on genomic determinants ofcancer susceptibility and outcomes. Under the direction of the Principal Investigator, develop and execute reproducible workflows for large-scale genomic, EHR-linked, and multi-omic data; performstatistical genetic analyses; interpret results; and contribute to collaborative scientific outputs. The position reports to the Professor, Oncology.

Develop, implement, and maintain reproducible computational workflows for analysis of large-scale human genomic data, includingwhole-genome/whole-exome sequencing, array-based genotype data, andderived genomic datasets.

Perform data quality control, extraction, transformation and integration across genomic, phenotype/EHR and multi-omic datasets using secure cloud and high-performance computing environments,including Hail/VDS-based workflows when appropriate.

Conduct statistical genetics and genetic epidemiology analysesunder PI direction, including variant- and region-based associationanalyses, population-stratified analyses, risk-score analyses, and related methods appropriate to individual projects.

Annotate and interpret genomic findings using internal and publicreference resources; evaluate technical and biological plausibilityand summarize results for scientific decision-making.

Maintain organized, documented, version-controlled code andanalytic records; perform validation and troubleshooting to supportreproducibility and efficient reuse of pipelines across projects.

Prepare analysis summaries, tables, figures, methods documentation and other materials for manuscripts, abstracts, presentations,grants, and project reports.

Perform other related duties as assigned.

Master’s degree from an accredited college or university in Bioinformatics, Computational Biology, Statistical Genetics,Genetic Epidemiology, Biostatistics, Data Science, or a related quantitative field.

Prior research experience analyzing human genomic or otherhigh-dimensional biological data including quality control,analysis, and interpretation of human genetic data derived fromwhole-genome sequencing, whole-exome sequencing, array genotyping,and/or other genomic technologies required.

Experience performing genomic data quality control, dataharmonization, variant annotation, and statistical analysis required.

Experience working with large population or clinical researchdatasets, including EHR-linked genomic cohorts preferred.

Graduate research and internships experience preferred; MSresearch project experience is acceptable.

Experience with Hail, SQL or relational databases, Git/version control, cloud-based genomic analysis, PRS, Mendelianrandomization, gene-environment interaction analysis or multi-omicsintegration preferred.

Experience with Python preferred.

Proficiency in R for data manipulation, statistical analysis, and visualization.

Working knowledge of Unix/Linux, shell scripting, and command-line bioinformatics tools.

Familiarity with common human-genetics data formats, includingVCF/BCF, PLINK BED/BIM/FAM or PGEN/PVAR/PSAM, BED interval filesand GWAS summary-statistic files.

Ability to work with large-scale genomic datasets incloud-computing or high-performance computing environments,including efficient management and analysis of datasets that cannotbe processed locally.

Familiarity with large genomic and population resources such asAll of Us, UK Biobank, dbGaP, gnomAD, 1000 Genomes andTOPMed.

Knowledge of human genetic association methods, including single-variant association testing, gene- or region-based analysis,population stratification, and ancestry adjustment, GWAS,rare-variant analysis and interpretation of associationresults.

Ability to develop clear, reproducible, well-documented…